Pub Date : 2024-01-13DOI: 10.1186/s13634-023-01100-4
Chao Ren, Jiayin Song, Mengxuan Qiu, Yingqi Li, Xianmei Wang
{"title":"Air–ground integrated artificial intelligence of things with cognition-enhanced interference management","authors":"Chao Ren, Jiayin Song, Mengxuan Qiu, Yingqi Li, Xianmei Wang","doi":"10.1186/s13634-023-01100-4","DOIUrl":"https://doi.org/10.1186/s13634-023-01100-4","url":null,"abstract":"","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"30 8","pages":"1-14"},"PeriodicalIF":1.9,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1186/s13634-023-01084-1
Jiayi Chen
{"title":"Multi-user communications for line-of-sight large intelligent surface systems","authors":"Jiayi Chen","doi":"10.1186/s13634-023-01084-1","DOIUrl":"https://doi.org/10.1186/s13634-023-01084-1","url":null,"abstract":"","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"104 1","pages":"1-24"},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138609120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1186/s13634-023-01085-0
Lantu Guo, Yuchao Liu, Yuqian Li, Kai Yang
{"title":"High-precision reconstruction method based on MTS-GAN for electromagnetic environment data in SAGIoT","authors":"Lantu Guo, Yuchao Liu, Yuqian Li, Kai Yang","doi":"10.1186/s13634-023-01085-0","DOIUrl":"https://doi.org/10.1186/s13634-023-01085-0","url":null,"abstract":"","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":" 55","pages":"1-16"},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.1186/s13634-023-01054-7
Yubin Fu, Xiaochuan Ma, Chao Feng, Xingxuan Pei, Pengzhuo Li
Abstract The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.
摘要浅海下低速目标的多普勒频移经常受到混响多普勒扩频杂波的干扰。杂波是由水下散射体产生的，增加了多普勒估计的难度。针对这一问题，本文提出了一种基于多普勒扩频杂波统计模型的混响目标分辨函数。该函数通过混响多普勒杂波的宽度，通过判断目标是否可分辨来调整波形参数。此外，混响多普勒扩频杂波具有时空变化特征，受掠掠角、波浪、风速、鱼群等影响。因此，声纳波形参数需要不断调整。因此，本文将基于强化学习的认知声纳与混响目标分辨函数相结合，评估不同环境下的不同波形。因此，声纳可以实时调整波形参数，在不同环境下获得最优波形。同时，本文对Dyna-Q强化学习的动作选择策略进行优化，提出了基于模型的最大动作选择Dyna-Q算法(Dyna-Q- max - action)。与传统的Dyna-Q和Q-learning算法相比，该算法所需的集数更少。最后，通过数值仿真验证了算法的有效性。
{"title":"Model-based optimal action selection for Dyna-Q reverberation suppression cognitive sonar","authors":"Yubin Fu, Xiaochuan Ma, Chao Feng, Xingxuan Pei, Pengzhuo Li","doi":"10.1186/s13634-023-01054-7","DOIUrl":"https://doi.org/10.1186/s13634-023-01054-7","url":null,"abstract":"Abstract The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"42 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1186/s13634-023-01075-2
Wei Liang, Jun Zhao, Yan Liu, Yan Liang, Jingwen Li
Abstract The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT.
{"title":"Fairness resource allocation based on blockchain for secure communication in integrated IoT","authors":"Wei Liang, Jun Zhao, Yan Liu, Yan Liang, Jingwen Li","doi":"10.1186/s13634-023-01075-2","DOIUrl":"https://doi.org/10.1186/s13634-023-01075-2","url":null,"abstract":"Abstract The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":" 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.1186/s13634-023-01073-4
Guangcai Wu, Yinglong Zheng
Abstract Electric charge service and management is an important part of electric power work. The effective recovery of the electric charge relates to the smooth development of daily work and continuous improvement of the operation and management of power supply enterprises. With the large-scale implementation of the card prepayment system, the problem of electricity customers defaulting on electricity charges has been solved to a large extent, but some large electricity users still fail to pay electricity charges on time. Therefore, under the current situation of power grid development, it is still necessary to strengthen the service and management of electricity charges to promote efficient recovery of electricity charges. Speech recognition technology has increasingly become the focus of research institutions at home and abroad. People are committed to enabling machines to understand human speech instructions and hope to control the machine through speech. The research and development of speech recognition will greatly facilitate people's lives shortly. The development of 5G technology and the proposal of 6G technology make the interconnection of all things not only a hope but also a reality. To realize the interconnection of all things, one of the key technical breakthroughs is the development of a new human–computer interaction sensing system. Under the guidance of relevant theories and methods, this paper systematically analyzes the user structure, electricity charge recovery management and service system, existing problems and causes in South China, and clarifies the necessity of design and application of electricity charge service system in South China power supply companies. The experimental data and empirical analysis results show that the optimized Bert fusion model can provide more digital support for the power supply companies in South China in terms of electricity charge recovery efficiency, management level system improvement, and electricity charge service.
{"title":"The construction of Bert fusion model of speech recognition and sensing for South China electricity charge service scenario","authors":"Guangcai Wu, Yinglong Zheng","doi":"10.1186/s13634-023-01073-4","DOIUrl":"https://doi.org/10.1186/s13634-023-01073-4","url":null,"abstract":"Abstract Electric charge service and management is an important part of electric power work. The effective recovery of the electric charge relates to the smooth development of daily work and continuous improvement of the operation and management of power supply enterprises. With the large-scale implementation of the card prepayment system, the problem of electricity customers defaulting on electricity charges has been solved to a large extent, but some large electricity users still fail to pay electricity charges on time. Therefore, under the current situation of power grid development, it is still necessary to strengthen the service and management of electricity charges to promote efficient recovery of electricity charges. Speech recognition technology has increasingly become the focus of research institutions at home and abroad. People are committed to enabling machines to understand human speech instructions and hope to control the machine through speech. The research and development of speech recognition will greatly facilitate people's lives shortly. The development of 5G technology and the proposal of 6G technology make the interconnection of all things not only a hope but also a reality. To realize the interconnection of all things, one of the key technical breakthroughs is the development of a new human–computer interaction sensing system. Under the guidance of relevant theories and methods, this paper systematically analyzes the user structure, electricity charge recovery management and service system, existing problems and causes in South China, and clarifies the necessity of design and application of electricity charge service system in South China power supply companies. The experimental data and empirical analysis results show that the optimized Bert fusion model can provide more digital support for the power supply companies in South China in terms of electricity charge recovery efficiency, management level system improvement, and electricity charge service.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"40 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135584463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.1186/s13634-023-01078-z
Seyed Mohammad Mehdi Yousefi, Seyed Saleh Mohseni, Hadi Dehbovid, Reza Ghaderi
Abstract Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by using Genetic Algorithm (GA) for feature selection. The simulations in this study aim to evaluate the convergence rate and estimation error of the filter. The results show that the proposed method achieves better results than other similar methods in tracking position in three different datasets. Moreover, the proposed method performs 8% better on average than other similar algorithms in night vision, cloud vision, and daylight vision situations.
{"title":"Tracking of moving human in different overlapping cameras using Kalman filter optimized","authors":"Seyed Mohammad Mehdi Yousefi, Seyed Saleh Mohseni, Hadi Dehbovid, Reza Ghaderi","doi":"10.1186/s13634-023-01078-z","DOIUrl":"https://doi.org/10.1186/s13634-023-01078-z","url":null,"abstract":"Abstract Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by using Genetic Algorithm (GA) for feature selection. The simulations in this study aim to evaluate the convergence rate and estimation error of the filter. The results show that the proposed method achieves better results than other similar methods in tracking position in three different datasets. Moreover, the proposed method performs 8% better on average than other similar algorithms in night vision, cloud vision, and daylight vision situations.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"30 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1186/s13634-023-01071-6
Jingyao Hou, Xinling Liu
Abstract The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories. However, most of the existing literature is confined to fully random measurement matrices, like random Gaussian and random sub-Gaussian measurements. This limitation often results in high generation and storage costs. This paper aims to apply semi-tensor product-based measurements to 1-bit CS. By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, thereby reducing the cost of generating and storing fully random measurement matrices. We propose a regularized model for this problem that has a closed-form solution. Theoretically, we demonstrate that the solution provides an approximate estimate of the underlying signal with upper bounds on recovery error. Empirically, we conduct a series of experiments on both synthetic and real-world data to demonstrate the proposed method’s ability to utilize a lower-dimensional measurement matrix for signal compression and reconstruction with enhanced flexibility, resulting in improved recovery accuracy.
{"title":"Semi-tensor product-based one-bit compressed sensing","authors":"Jingyao Hou, Xinling Liu","doi":"10.1186/s13634-023-01071-6","DOIUrl":"https://doi.org/10.1186/s13634-023-01071-6","url":null,"abstract":"Abstract The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories. However, most of the existing literature is confined to fully random measurement matrices, like random Gaussian and random sub-Gaussian measurements. This limitation often results in high generation and storage costs. This paper aims to apply semi-tensor product-based measurements to 1-bit CS. By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, thereby reducing the cost of generating and storing fully random measurement matrices. We propose a regularized model for this problem that has a closed-form solution. Theoretically, we demonstrate that the solution provides an approximate estimate of the underlying signal with upper bounds on recovery error. Empirically, we conduct a series of experiments on both synthetic and real-world data to demonstrate the proposed method’s ability to utilize a lower-dimensional measurement matrix for signal compression and reconstruction with enhanced flexibility, resulting in improved recovery accuracy.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"21 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1186/s13634-023-01076-1
Weilin Zhang, Lingyi Wang, Hangtao Mao, Zi Wang, Wei Wu
Abstract Reconfigurable intelligent surface (RIS) is an emerging technology to improve the spectral efficiency of wireless communication systems. However, the high complexity of beam design and the non-negligible overhead associated with RIS limit the number of elements that can be deployed in practice. In this paper, we investigate the downlink communications of irregularly deployed intelligent reflecting surfaces that assist non-orthogonal multiple access (NOMA) systems. To address this challenge, we propose a novel four-step resource allocation algorithm. Specifically, we first obtain a sub-optimal solution for the sparse deployment of RIS elements using a Simulated Annealing Algorithm. We then solve the power allocation problem by employing an integer optimization algorithm that continuously iterates the immobile point. To simplify and optimize the reflection coefficient matrix, we propose a construction inequality algorithm. Finally, we optimize the channel assignment using a genetic algorithm. The simulation results demonstrate that the proposed irregular RIS-assisted NOMA system outperforms the traditional RIS-assisted orthogonal multiple access system, with a maximum throughput increase of approximately 30%.
{"title":"Throughput maximization for irregular reconfigurable intelligent surface assisted NOMA systems","authors":"Weilin Zhang, Lingyi Wang, Hangtao Mao, Zi Wang, Wei Wu","doi":"10.1186/s13634-023-01076-1","DOIUrl":"https://doi.org/10.1186/s13634-023-01076-1","url":null,"abstract":"Abstract Reconfigurable intelligent surface (RIS) is an emerging technology to improve the spectral efficiency of wireless communication systems. However, the high complexity of beam design and the non-negligible overhead associated with RIS limit the number of elements that can be deployed in practice. In this paper, we investigate the downlink communications of irregularly deployed intelligent reflecting surfaces that assist non-orthogonal multiple access (NOMA) systems. To address this challenge, we propose a novel four-step resource allocation algorithm. Specifically, we first obtain a sub-optimal solution for the sparse deployment of RIS elements using a Simulated Annealing Algorithm. We then solve the power allocation problem by employing an integer optimization algorithm that continuously iterates the immobile point. To simplify and optimize the reflection coefficient matrix, we propose a construction inequality algorithm. Finally, we optimize the channel assignment using a genetic algorithm. The simulation results demonstrate that the proposed irregular RIS-assisted NOMA system outperforms the traditional RIS-assisted orthogonal multiple access system, with a maximum throughput increase of approximately 30%.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"39 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135320792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1186/s13634-023-01074-3
Yifan Li, Baihua Shi, Feng Shu, Yaoliang Song, Jiangzhou Wang
Abstract As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has $$6^{circ }$$ 6∘ advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at $$textrm{SNR}=-8$$ SNR=-8 dB and the number of snapshots $$N=100$$ N=100 , this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.
大规模MIMO是未来第六代(6G)通信的关键技术之一，大规模天线阵列由于能够提供更大的孔径和更高的估计分辨率，在到达方向(DOA)估计中得到了广泛的考虑。然而，传统的全数字架构需要每个天线一个射频(RF)链，这对于高硬件成本和大量射频链造成的更多功耗来说是具有挑战性的。为此，提出了一种基于重叠子阵列(OSA)架构的混合大规模MIMO阵列，在降低硬件成本的同时，相对于非重叠子阵列(NOSA)架构具有更好的DOA估计精度。仿真结果还表明，所提出的OSA结构的精度比信噪比为10 dB的NOSA结构有$$6^{circ }$$ 6°的优势。此外，为了提高DOA估计分辨率，将卷积降噪自编码器(CDAE)与深度神经网络(DNN)相结合，提出了一种基于深度学习的DOA估计器，CDAE可以去除样本协方差矩阵(SCM)的近似误差，DNN可以进行高分辨率DOA估计。从仿真结果来看，CDAE-DNN在$$textrm{SNR}=-8$$信噪比= - 8 dB，快照个数$$N=100$$ N = 100时可以达到精度下界，这意味着CDAE-DNN在通信较差的情况下具有更好的性能，并且与传统估计器相比可以节省更多的软件资源。
{"title":"Deep learning-based DOA estimation for hybrid massive MIMO receive array with overlapped subarrays","authors":"Yifan Li, Baihua Shi, Feng Shu, Yaoliang Song, Jiangzhou Wang","doi":"10.1186/s13634-023-01074-3","DOIUrl":"https://doi.org/10.1186/s13634-023-01074-3","url":null,"abstract":"Abstract As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has $$6^{circ }$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>6</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at $$textrm{SNR}=-8$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mtext>SNR</mml:mtext> <mml:mo>=</mml:mo> <mml:mo>-</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:math> dB and the number of snapshots $$N=100$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>100</mml:mn> </mml:mrow> </mml:math> , this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"35 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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